Enhancing the Prediction Accuracy of Data-Driven Models for Monthly Streamflow in Urmia Lake Basin Based upon the Autoregressive Conditionally Heteroskedastic Time-Series Model

Enhancing the Prediction Accuracy of Data-Driven Models for Monthly Streamflow in Urmia Lake Basin Based upon the Autoregressive Conditionally Heteroskedastic Time-Series Model

Hydrological modeling is one of the important subjects in managing water resources and the processes of predicting stochastic behavior. Developing Data-Driven Models (DDMs) to apply to hydrological modeling is a very complex issue because of the stochastic nature of the observed data, like seasonali...

Full description

Bibliographic Details
Main Authors: Nasrin Fathollahzadeh Attar, Quoc Bao Pham, Sajad Fani Nowbandegani, Mohammad Rezaie-Balf, Chow Ming Fai, Ali Najah Ahmed, Saeed Pipelzadeh, Tran Duc Dung, Pham Thi Thao Nhi, Dao Nguyen Khoi, Ahmed El-Shafie
Format: Article
Language:English
Published: MDPI AG 2020-01-01
Series:Applied Sciences
Subjects:
data-driven models
monthly streamflow
arch-type hybrid models
urmia lake basin
Online Access:https://www.mdpi.com/2076-3417/10/2/571

Internet

https://www.mdpi.com/2076-3417/10/2/571

Similar Items